ArticleRisk analysis : an official publication of the Society for Risk Analysis2019
Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.
Article in Risk analysis : an official publication of the Society for Risk Analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- The Changing Landscape of Antibiotic Treatment: Reevaluating Treatment Length in the Age of New Agents.Antibiotics (Basel, Switzerland) · 2025Review
- Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A review of model evaluation metrics for machine learning in genetics and genomics.Frontiers in bioinformatics · 2024Review
- Comparison of Source Attribution Methodologies for Human Campylobacteriosis.Pathogens (Basel, Switzerland) · 2023Article
- Development and validation of a random forest algorithm for source attribution of animal and humanFrontiers in microbiology · 2023Article
- Machine learning to predict foodborne salmonellosis outbreaks based on genome characteristics and meteorological trends.Current research in food science · 2023Article
- Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.Briefings in bioinformatics · 2022Review
- A Machine Learning Model for Food Source Attribution ofPathogens (Basel, Switzerland) · 2022Article
- Source Attribution of Human Campylobacteriosis Using Whole-Genome Sequencing Data and Network Analysis.Pathogens (Basel, Switzerland) · 2022Article
- Age-Related Changes in Lipid and Glucose Levels Associated with Drug Use and Mortality: An Observational Study.Journal of personalized medicine · 2022Article
- Machine learning random forest for predicting oncosomatic variant NGS analysis.Scientific reports · 2021Article
- Microbiota long-term dynamics and prediction of acute graft-versus-host disease in pediatric allogeneic stem cell transplantation.Microbiome · 2021Article
- Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case ofMicroorganisms · 2020Article
- Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium.Risk analysis : an official publication of the Society for Risk Analysis · 2020Article
- AB_SA: Accessory genes-Based Source Attribution - tracing the source ofMicrobial genomics · 2020Article
- Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms.EFSA journal. European Food Safety Authority · 2019Article
- Privacy and ethical challenges in next-generation sequencing.Expert review of precision medicine and drug development · 2019Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready-to-eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.
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